Artificial Intelligence in Biomedical 3D Printing: Mapping the Evidence

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Detalles Bibliográficos
Publicado en:	Journal of Manufacturing and Materials Processing vol. 9, no. 12 (2025), p. 407-440
Autor principal:	Tănase, Maria
Otros Autores:	Veres, Cristina, Szabo Dan-Alexandru
Publicado:	MDPI AG
Materias:	Smart materials Polymers Citations Datasets Evolution Boolean Advanced manufacturing technologies Multidisciplinary research Interdisciplinary aspects Mapping Three dimensional printing Machine learning Synthesis Shape memory Additive manufacturing Design techniques Bibliometrics Artificial intelligence Prediction models Cocitation Health care Publications Biomedical engineering 3-D printers Tissue engineering Data collection Algorithms Tissues Deep learning Design optimization Precision medicine
Acceso en línea:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Descripción
Resumen:	This study provides an integrated synthesis of Artificial Intelligence (AI) applications in Biomedical 3D Printing, mapping the conceptual and structural evolution of this rapidly emerging field. The bibliometric analysis, based on 229 publications indexed in the Web of Science Core Collection (2018–2025) and visualised in CiteSpace, identifies three interconnected research domains: AI-driven design and process optimisation, data-assisted bioprinting for tissue engineering, and the development of smart and adaptive materials enabling 4D functionalities. The results highlight a clear progression from algorithmic control of additive manufacturing parameters toward predictive modelling, deep learning, and autonomous fabrication systems. Leading contributors include China, India, and the USA, while journals such as Applied Sciences, Polymers, and Advanced Materials act as major dissemination platforms. Emerging clusters around “4D printing”, “deep learning”, and “shape memory polymers” indicate a shift toward intelligent, sustainable, and personalised biomanufacturing. In addition, a qualitative synthesis of the most influential papers complements the bibliometric mapping, providing interpretative depth on the scientific core driving this interdisciplinary evolution. Overall, the study reveals the consolidation of a multidisciplinary research ecosystem in which computational intelligence and biomedical engineering converge to advance the next generation of adaptive medical fabrication technologies.
ISSN:	2504-4494
DOI:	10.3390/jmmp9120407
Fuente:	ABI/INFORM Global